International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 06 Issue: 02 | Feb 2019
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Process of Detecting Chronic Diseases by using
Photoplethysmogram- A Review
Shahrukh AG. Sheikh1, Muzaffar Z. Khan2
1,2Department
of Electronics and Communication Engineering, Anjuman College of Engineering, Nagpur,
Maharashtra, India
---------------------------------------------------------------------***---------------------------------------------------------------------Abstract - The aim of this paper is to detect the different
types of chronic diseases with the help of the
photoplethysmography(PPG).A literature review is made to
detect the different types of chronic diseases such as
diabetics, hypertension, heart rate(HR),arterial stiffness, etc.
PPG is used to estimate the blood flow of the skin by using
infrared light. Various researchers from different domains
have interested only because of their advantages such as
inexpensive, non-invasive and easy to set up. There are two
different types of methods to calculating HR,
Electrocardiogram(ECG) and PPG. The ECG signal in which a
wave graph that records electrical activity of heart. By
detecting peaks of waveforms of ECG or PPG we get details of
heart beat.
Key
Words:
Photoplethysmography,
Hypertension,
Heart
Rate,
Arterial
Electrocardiogram.
That’s because people with diabetes, particularly type 2
diabetes, because of this conditions that contribute to their
risk for developing cardiovascular disease. High blood
pressure (hypertension) has long been recognized as a major
risk factor for cardiovascular disease. When the patients have
both i.e. hypertension and diabetes, which is a common
combination, then there is a risk for cardiovascular disease is
much greater.
2. LITERATURE REVIEW
D.W.Kim, S.Wookim(2007) discuss about the population of
diabetes is increasing very fast
due to economic
development and lifestyle changes. Diabetes mellitus (DM)
which is related to the defective of insulin secretion and/or
function is a chronic metabolic disease. Many organs
including kidney, heart, muscle, eyes and other organs are
affected due to the long period of diabetes, which is
associated with the tissue damage and damage of the blood
vessels. They use the photoplethysmography (PPG) and
Laser Doppler (LD) to measure the blood volume changes
and perfusion of fingers and toes. They suggested PPG
method provides good reproducibility, sensitivity. They also
prove that it is simple, low cost, reliable non-invasive which
could become an effective for early detection of diabetic.
Diabetics,
Stiffness,
1. INTRODUCTION
The word plethysmograph is a combination of two different
Greek words ‘plethysmos’ means increase and ‘graph’
which is the word for write and photo means light. The set
up of PPG is very easy, convenient, simple and economically
efficient. It uses a probe which contains a light source and
detector to detect cardio-vascular pulse wave that
propagates through the body.[1]The PPG signal reflects the
blood movement in the arteries, which goes from heart to
the fingertips through the blood vessels in a wave-like
motion.[2]
K.C.Lan, P.Raknim(2018) says that the early prediction is
possible when we collecting PPG-based information. They
use a data mining technique to estimate Heart Rate
Variability (HRV) and they predict diseases. Heart rate
variability(HRV) is used to overcome the risk of
cardiovascular disease and have also obtain the data via
electrocardiography(ECG).collecting heart rate via
Photoplethysmography (PPG) is now a lot easier. By
detecting peaks in the waveforms in either ECG or PPG, we
can very easily obtain the details of a person’s heart beat
information. The beat to beat interval is measured by peak to
peak interval. In an ECG wave this is known as the RR
Interval, where R is the point which corresponding to the
peak of the QRS complex. They focus on the prediction of
hypertension. They use six HRV parameters to predict
hypertension, they are present below and they said that it is
possible to predict the disease by collecting the PPG-based
heart rate information.
The PPG Signals consists of AC and DC components. The AC
components is synchronous with the heart rate and depends
on the pulsatile volume changes. The DC component of the
signal varies slowly and reflects variations in the blood
volume of the examined tissue. There is variation in the
wave due to aging of the artery which leads to stiffening.
RELATION BETWEEN DIABETES AND HEART DISEASE
The connection between diabetes and heart disease starts
with the high blood sugar levels. over the long period, the
high glucose in the blood stream can damages the arteries,
causing them to become stiff and hard. A Fatty materials that
builds up on the inside of the blood vessels, a condition is
known as atherosclerosis. This can block the blood flow to
the heart or brain, therefore leading to heart attack or stroke.
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International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 06 Issue: 02 | Feb 2019
p-ISSN: 2395-0072
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Table -1: Descriptions of the HRV parameters.
SDNN
Standard deviation of all RR (NN)
intervals
SDNN
The std deviation of average NN
intervals calculated over short periods
Mean RR
Mean of all RR intervals
RMSSD
Square root of mean of the square of all
NN interval differences during a time
period
LF/HF
Low to high frequency power ratio.
LFmu
Normalized low frequency power of the
HRV.
HFmu
Normalized high frequency power of the
HRV.
3. METHODOLOGY
PPG
S.S.Mousavi, M.Firouzmand (2109) do their work for
measuring the Blood Pressure(BP) which is one of the four
vital signals that provides valuable medical information about
the cardiovascular. In recent years, various studies have been
conducted on non-invasive and cuff-less BP estimation by
using the photoplethysmography(PPG)signals. S.S.Mousavi
gives a new method for estimating the mean arterial pressure
(MAP), Diastolic blood pressure (DBP) and Systolic blood
pressure (SBP) by using only the PPG signals. They uses raw
value of the PPG signals at a given time interval for estimating
BP. They used parameter based algorithms and use the
features which are extracted from PPG signals in time or
frequency domain. The results are also very close to the
standard boundary with an average error close to zero for
SBP estimation. Also, according to the British Hypertension
society (BHS) standard, the proposed algorithm for DBP
estimation got grade A.
As shown in above flow chart, any PPG diagnostic
structure consists of three stage pre-processing, features
extraction, classification.
a. PRE-PROCESSING IN PPG SIGNALS.
The quality of the PPG signal depends on the various
factors such as location and the properties of the subject’s
skin at measurement that also include the individual skin
structure, the blood oxygen saturation, blood flow rate, skin
temperatures and the measuring environment. PPG sensors
optically detect changes in the blood flow volume via
reflection from through the tissue. The changes in the light
intensity are associated with small variations in blood
perfusion.
b. PPG FEATURES EXTRACTION AND ITS APPLICATIONS
S.Usaman, M.B.Reaz done the work in the characteristics of
photoplethysmogram(PPG) has been investigated noninvasively to measure the condition of arterial stiffness. They
will work on a total 101 type 2 diabetic patients and aged is
from 50 to 70 year were participated in their study. The
patients divided into two groups which Group1 consist of
patients with HbAlc<8% and those with HbAIC>10% is in
Group 2.An area under curve has been investigated and
chosen as PPG parameter. An independent sample t-test used
to compare an auc-PPG between two levels of HbAlc which
are HbAlc<8%.The results have explained that changes in
arterial properties can be non-invasively detected by
analyzing pulse shape characteristics. An auc-PPG promises a
potential technique for analyzing shape with dicrotic notch.
PPG signals were recorded using commercial devices and
they were analyzed off-line by using MATLAB. With the help
of pulse we extract the important features from that PPG
Signals. Here we will take the standard wave form of the PPG
signals for the analysis and give information of some
terminology .The PPG pulse is commonly divided into two
phases: the anacrotic phase is the rising edge of the pulse,
another is the catacrotic phase is the falling edge of the pulse
as shown in figure below.
Fig 1: waveform of PPG
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The first phase is primarily concerned with systole and the
second phase with diastole and wave reflections from the
periphery. From the PPG wave we can also understand some
terminology which is as given below.

Peak to peak intervals:-The distance between two
consecutive systolics peak will be referred to as
peak-peak interval.

Pulse Interval:-The distance between the beginning
and the end of the PPG waveform.
The second derivative of the finger PPG waveform is used to
stabilize the baseline and enable the individual features to be
visualized and detected easily. The waveform includes four
systolic waves and one diastolic wave namely a-wave(early
systolic positive wave),b-wave(early systolic negative
wave),c-wave(late systolic reincreasing wave),d-wave(late
systolic redecreasing wave) and e-wave(early diastolic
positive wave).The e-wave represents the dicrotic notch as
shown in figure.
4. CONCLUSION
This review discussed the photoplethysmography technology
and also give the applications of PPG with the help of
literature survey. A common structure of any PPG diagnostic
system consists of three stages preprocessing, feature
extraction, and classification. The main focus of this review
was the preprocessing and feature extraction stages. In the
feature extraction stage, the characteristics of the PPG
waveform and its derivatives have been clarified.
Photoplethysmography is a very reliable technology due to its
simplicity, low cost and non-invasive. It has potential for
early screening for various chronic diseases such as Heart
Rate, Hypertension, Diabetics, arterial stiffness, etc. However,
a full understanding of the diagnostics value of the different
features is still lacking and more research is needed.
Fig 2: two consecutive PPG waves
Second derivative photoplethysmogram (SDPPG) which is
one of the application of PPG for detecting the arterial
condition. So, before going to SDPPG we see First derivative
photoplethysmogram.
REFERENCES
[1]
Mohamed Elgendi(2012,) “on the Analysis of fingertip
photoplethysmogram”, Bentham science publishers.
[2]
Deok won kim, sung wookim, soo chain kim, Eum seok
kang(2007) , “Detection of diabetic Neuropathy using
blood volume ratio of finger and toe by ppg”
[3]
Sahnius bt usman, mohd Alauddin bin mohd Ali,
Kalaivani chellapan(2009), “second Derivative of
photoplethysmogram in Estimating vascular aging
among diabetic patients”Research university grant:UKM
ΔT:-T is the peak-to-peak time which is related to the time
taken for the pressure wave to propagate from the heart to
periphery and back. The time between the systolic and
diastolic peaks is ΔT.
[4]
Kun-chan Lan, Paweeya Raknim, wei-Fong Kao(2018),
“Toward Hypertesion Prediction Based on PPG Derived
HRV Signals: a Feasibility Study”jounrnal of medical
system.
Second derivative photoplethysmogram (SDPPG)
[5]
Seyedeh
somayyeh
mousavi,mohammad
firouzmand(2019), “Blood pressure estimation from
appropriate and inappropriate PPG signals using
Awhole-based method”journal of biomedicak signal
processing and control ELSEVIER 2019.
[6]
Yongbo Liang, Zhencheng chen(2018), “Hypertension
Assessment using Photoplethysmography:A Risk
Stratification Approach”journal of clinical medicine.
[7]
Mohamed Elgendi, Yongbo Liang and Rabab
ward(2018), “Toward Generating more Diagnoltic
Features From Photoplethysmogram waveforms”
Fig 3: first derivative wave of PPG
Crest time (CT): crest time is the time from the foot of the
PPG waveform to its peak which is see in figure.
Fig 4: second derivative wave of PPG
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[8]
International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 06 Issue: 02 | Feb 2019
p-ISSN: 2395-0072
www.irjet.net
Swathin Ramasahayan, Sri Haindavi(2013,) “Non
Invasive Estimation of Blood Glucose Using Near Infra
red Spectroscopy and Double Regression Anaiysis”
Seventh International Conference on Sensing
Technology.
BIOGRAPHIES
Shahrukh AG. Sheikh
Ramaleshwar ward, Ramtek
M-tech Scholar at Anjuman College
of Engineering, Nagpur
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